If your products and services don’t serve the data science community; however, you’re using data science in your products and services for a competitive advantage, you’re in a popular but challenging situation with your customers. I call what I’ve just described: using data science as a supporting strategy. For instance, the people at Graze.com incorporate data science into their snack business to develop and deliver the next box of goodies their customer will get. Let’s be clear though: they’re in the snack business, not the data science business. In this situation, I recommend keeping your data scientists as far away from your customers as possible. If you’re using big data as a supporting strategy, make it a priority to keep your customers insulated from your data science.

Buffering

Buffering is an important strategy for leaders using data science as a supporting strategy. In short, buffering is structuring at least one organizational layer between your data science team and your customers. Contrast this to leaders using data science as a core strategy–selling products and services to other data scientists, like RapidMiner, Kitenga (now part of Dell), and Cloudera. In this case, it’s a great idea to put your data science team in front of your customers, because like attracts like. However, Graze.com’s snackers have no interest in data science, so in this case, keep the analytics out of the conversation.

Instead, have your customers interface with other people in your company who are like them. The same “like attracts like” concept applies. If you’re in the business of wearables for athletic people, put a layer of athletic-minded people between your customers and your data science team. A good friend of mine is a triathlete that runs analytics to help other triathletes compete. Although he’s an analytic, he wears his triathlete persona when addressing his customers. Since he’s a one-man shop, that’s his only choice. In a larger company, this concept should obtain as a sales and marketing layer comprised of athletes–not engineers.

Translation

One important job of the buffering organization is to translate what the data scientists are trying to accomplish, into terms your customers understand. The reason why you don’t put data scientists in front of non-analytics, is that they’re typically difficult to relate to. Imagine a group of pro football players showing up at Comic-con. The first time a trekkie introduces themself to a linebacker in Klingon, there will be a problem. Before a product or service is introduced to your customers, it must be sanitized from its analytic underpinnings.

When Progressive talks to its clients about its SnapShot device, there’s no discussion about analytics. Their marketing may allude to the scientific prowess that goes into their product for effect; however, in practice they call it usage-based insurance. This is a perfect example of translation. Most drivers understand the term usage-based insurance. You’ll quickly lose them if you start talking about behavior-based digital profiling using a synthesis of regression and machine learning algorithms.

It may take multiple layers within the organization to successfully translate your analytic-based competitive advantage into customer-facing language. I’ve worked with several organizations where the developers are three or four levels removed from the customer. When I worked with Visa, there was a product development group, product function group, business analyst group, and then developers and architects. Sometimes it takes multiple translations to get it right for the customer.

Curating

Curating is a special requirement for those integrating advanced analytics into their products and services. A special challenge the buffering organization has with their analytic brain trust is information overload. Curating sifts through the piles of brilliance to extricate the golden nuggets that will appeal to your customers. That’s no easy feat.

Consider a museum curator whose job is to process archeological findings into a display of wonderment. Piles and piles of ancient bones, tools, and artifacts must be reduced, organized, and displayed in a way the appeals to the masses. Curators do more than just translate–they manage and oversee their body of work, and interact with the viewing public.

In a similar fashion, your curators must own the body of work produced by your data science team. Whether or not you put your curators in direct contact with your customer (both ways work), they should synthesize the wealth of information produced by your data scientists into a concise, attractive package that your customers will relate to. Even if you translate well, if you don’t curate, you’ll hit your target market with too much information and they’ll find a competitor that’s easier to understand.

Summary

There’s no doubt your data scientists are brilliant; however, too much brilliance for your uninitiated customers will drive them away. If you incorporate fancy analytics into your products, but your customers aren’t really jazzed by math and science, save the tech-speak for your in-house design team. As you structure your organization, ensure there’s a buffer between your data science team and your customers, who can translate and curate their findings. If I’m a Graze.com customer, I don’t want a lecture on how to design the perfect meal–I just want a snack.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics Blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

I'm sure you are; it's human nature. A client recently told me that she came home one day and noticed that there was a water-filled glass sitting directly on a wood table. She asked her husband, "Where is the coaster for this glass?" Her husband responded, "That's what you noticed? I just finished cleaning the entire house!"

I see a lot of leaders frustrated with their data science team. They've spent a lot of money so the have very high expectations. In consulting, we call that White Knight Syndrome, and I deal with it all the time. So when things don’t go as expected, they go down a very classic route of identifying gaps and solving problems. Not only is this enervating, but it's a reckless abuse of your data science team's potential. It's far better to build on the strengths of your data science team, than it is to improve on their weaknesses. Here are five things to absolutely love about your data scientists.

They Fuel An Uncatchable Competitive Advantage

Your data science team is a key ingredient for a breakthrough competitive advantage. This is no joke; so don't ever overlook this fact. They tackle unsolvable problems for fun, in a way no other profession can. Most people take for granted how the data scientists at Google have changed the world, with a search engine that was late to the party. Sure, the leaders had the vision that powerful search capabilities would equate to market domination; however, it was the data scientists that figured out to jump into our brains, figure out what we were trying to find, and bring back the most relevant results. Google's data scientists made it one of the most powerful organizations in the world.

They're A+ Students In School and Life

Data scientists learn fast and retain extremely well. They've done it their whole lives. Most data scientists you encounter excelled in school—4.0 GPA in high school and college. And although you would expect them to get good grades in computer science and math, remember that a computer science degree has more than just computer science classes. Data scientists don't only get good grades in math and science; they get good grades in everything. Don't be shy about bringing them into your business world. They'll start contributing real value faster than you realize.

They Deliver No Matter What

Data scientists are extremely loyal under the right conditions--sometimes to a fault. I can't count the number of times I've been roped into an all-nighter because of situations far out of my control. We dig in and we deliver anyway; it's part of that excellence gene that I referenced earlier. The only thing you need to do is setup the right conditions, which has more to do with job satisfaction than money (although a good paycheck doesn't hurt either). Data scientists love to create data masterpieces with people they enjoy. With the right environment and the right challenge, they'll stay with you all the way.

They Are A Magnet For Other Talent

It seems like everybody's having a hard time finding good data scientists, except for other data scientists. If you're a leader, you probably know a lot of other leaders; so, guess who data scientists hang out with? You guessed it--other data scientists. This is important to you on a number of levels. If you ever need to extend your team, the best source for finding more data scientists is the team you already have. Also, the data scientist community is very supportive. So if your team actually gets stuck on a problem, there's a huge brain trust at their disposal that's ready and willing the help.

They Save Your From Yourself

Data scientists think through everything before making a decision. This will and should drive you crazy if you're an impulsive leader. Impulse is good for immediate action, but like all things the best results come from Aristotle's golden mean--the desirable middle between two extremes. At one extreme is a knee-jerk reaction that gets you into trouble (sound familiar?) and at the other extreme is analysis paralysis. The trick is to get the right balance, and you won't do that without the counsel and reason of your data scientists. You may think you have a good idea, but it won't sit right with your data scientists until there's data, research, and analysis. This voice will save your assets more times than not.

Summary

Identifying problems and closing gaps with your data science team will only bring you status quo; however, identifying strengths and raising the bar will catapult you to a place nobody can catch. Instead of obsessing about what's wrong; invigorate your organization by using the strengths within your data science team. There's a lot to love: they're extremely bright, loyal, and precise. Make this your starting point and enjoy your immaculate house, instead of worrying about a missing coaster.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics Blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

Does it feel like you're spinning on your next big product idea instead of moving forward? That's a very expensive scenario when a data science team is involved.

I'm often called into companies to organize them and move them forward. Most of the time, they have an idea of what they want to do, but for some reason, they just can't move things forward. There's a lot of activity, and a lot of meetings, but no real accomplishments. Does this sound familiar?

There are several reasons why this happens, but all comes back to execution excellence, which is not intuitive or intentionally developed as a capability in most organizations. Even with great thinkers and doers, if you don't have a good frame for moving an idea into action, you'll probably spin. However, if you're focused and organized, your data science team can begin work on your next big idea in just five days.

It Starts With Leadership

The first day starts with you--the leader. If your organization is spinning around, my guess is that you're trying to get too many things done at once. If your next big idea is really important, your first job is to decide that it takes priority over everything else. You must resolve this for yourself before engaging with the rest of the team.

Once you've resolved that this is where your organization will focus, develop logical and emotional reasons why everyone should make the development of this product their priority. I had a leader tell me if they don't differentiate somehow, they're going to die. That's compelling and emotional! This is the message that you want to move forward with.

Start With the End In Mind

On day two, in the spirit of the advice given to us by the late Dr. Stephen Covey, start with the end in mind. Define what success looks like with your leadership team. This can take an hour or it can take all day--but it shouldn't take more than a day. The outcome of this exercise is more than a vision statement; it's a vivid depiction of how the future will look. I recommend doing this in three cycles: macro-environment, competitive environment, and internal environment; in that order.

In the first cycle, paint an outline of your future macro-environment, including political, economic, social, technological, environmental, legal, and other factors that affect your company. Fill in this outline on the second cycle with your competitive environment, including: customer, suppliers, new entrants, and alternative offerings. Finally, complete the vision on the third cycle with how your organization will look, including size, composition, culture.

You've Got The Brains, Now Start Storming

On day three, involve your entire data science team in a brainstorm. The goal is to understand how the team will achieve the vision. The pre-work on days one and two are important. Open the meeting with the logical and emotional reasons why this effort is more important than anything else they're working on and clearly articulate your vision.

During your brainstorm, let the ideas flow. Encourage free flow of thought, and capture ideas in an organic fashion (in a mind mapping tool) and not in a linear fashion. Most brainstorms like this will last a few hours, so make sure to incorporate breaks. When I reach most organizations, they've started here and they're stuck here because nobody's defined a cutoff period. You're cutoff period is the end of the workday--after day three, there will be no more brainstorming.

Making Sense Of It All

Bring the team back on day four to organize everything. It's important to reinforce the sequence--we're done with guidance, we're done with visioning, and we're done with brainstorming. Don't let the team regress at this point--that's how everything goes circular. The team must mentally switch modes from brainstorm to organize.

Organizing is about grouping and removing duplicates. This can be time consuming for some; however, it’s easier for data scientists. They are naturally adept at separating ideas into affinity groups. You should reduce the ideas in your brainstorm into tangible deliverables; this will be the basis for your action plan. One more day to go.

Moving Forward

Bring everybody back on day five to build an action plan. Set the expectation that by the end of the day, work will begin. Divide the day into two parts. The first part of the day is spent identifying the top priority deliverables (from the action plan) and when they will be done.

The second half of the day is a working session to get started on the top priority deliverable. While the data scientists are moving forward, the analytic manager completes the action plan and the change leader is starts on the stakeholder map. If you want to move forward within five days, schedule it into the agenda for day five.

Summary

If you have a great idea, and you have a data science team, you should be getting things done and not meeting to schedule more meetings. I've given you a simple, five-day agenda for moving forward. It starts with a resolution you make with the man in the mirror--so take that first step. If everything's a priority then nothing's a priority. Make this the priority, and in five days you'll be well on your way to the next level.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

If you're creating a product or service that incorporates data science and big data analytics, you might be paying too much attention to artificial intelligence and not enough attention to superficial intelligence. Data science is filled with mystical algorithms reminiscent of spells chanted by wizards of yore. Armed with this arsenal of prestidigitation, zealous leaders eagerly present their market with new and improved widgets, powered by artificial intelligence. However, many times they take an egocentric view of the world, relying myopically on their internal capabilities for advanced analytics. If you flip this around to a customer-centric view, you'll see intelligence doesn't need to be artificial to be valuable. To get the most value from your artificial intelligence application, combine it with the superficial intelligence obtained by involved communities.

The Wisdom Of The Crowd

There's a wealth of valuable data available in plain sight and happening right now--I call this superficial intelligence. When I was in grade school, my neighborhood friends and I would occasionally start a pickup football game in the middle of the street. We would post the girls on the corner to signal us when a car was coming, so we could move out of the street. This was great superficial intelligence for us. Without the benefit of this information, a wide receiver might be tackled by an unwelcome, automotive defensive back!

Superficial intelligence is a great addition to your bag of data science tricks, as it adds to your existing base of artificial intelligence and it represents a more customer-centric marketing approach. This primarily applies to leaders who are using big data analytics to support their core products and/or services: similar to Progressive Insurance's Snapshot device, where analytics supports a traditional product (insurance) to gain a competitive advantage. The value of data and information doesn't need to be artificial or involve sophisticated analyses to be valuable. Just knowing that a car was turning down our street was great to know. Where this starts to get exciting for data scientists is when you combine superficial intelligence with artificial intelligence. That will take your game to whole new level.

A great example of this is an application I just downloaded on my iPhone called Waze. If you haven't heard of it yet, you really should. Like Google Maps or MapQuest, Waze is an application that helps you navigate the streets of your locale. You give it an address, mount your phone in your car, and it gives your real-time navigation instructions to your destination. What's different about Waze though, is the Waze community, which is actively involved in feeding you superficial data. For instance, with the help of your local community, Waze tells you where there's an accident, construction that requires a detour, or even a cop hiding out under a bridge. Waze combines this information with real-time analytics to determine your best route. It's amazingly powerful and accurate. I don't say this often, but it actually puts Google to shame. That's what the wisdom of the crowd can do for you.

The Human Machine Synergy

To apply this principle of combining artificial and superficial intelligence, consider the evolution of data into wisdom. I'd say superficial intelligence gives you a good base of data to start with. Remember, data is just raw, uncultured insights. If there's an accident a half-mile away or a car around the corner, that's really good data that someone could use. You can combine this with non-crowd-sourced data. Waze obviously has geographic data at its immediate disposal and I'm sure the team at Waze curates of wealth of other information as well. This data becomes useful when it evolves into information.

Information is analyzed and applied data. When Waze analyzes all the stock and superficial data coming from the Waze community and tells you to "turn right," that's information. Information tells your consumer what to do with all this data, based on their objectives. So again, you must transcend the pure data paradigm and think about what your customers might be trying to accomplish. Then, using a mix of base data and superficial data, perform a real-time, big data analysis to prescribe their next step. This strategy alone puts you at a distinctive advantage, but there is one more level you can take it to.

Information evolves into knowledge, which further evolves into wisdom. Knowledge is when you take information from disparate sources and combine them for new insights. With superficial intelligence, you're already going down this path; however, for more impact, you'll want to explore related but very different sources of information. I used to live next to an arcade, which would sometimes host special events that drew a lot of traffic. So, it wouldn't be a good idea for a pickup game on one of these days due to the traffic. Wisdom comes from maturing knowledge over time. The first time we tried a pickup game at 5p when everyone was coming home from work, we learned our lesson. If you apply these ideas to your next product or service, you will probably be approaching breakthrough territory.

Summary

Artificial intelligence is great, but when combined with the superficial intelligence of the crowd, your product or service goes to a whole new level. Take some time to consider how your existing data can benefit from additional, crowd-sourced data, and what your analytics would look like at that point. Then, survey your customers and see if they would be willing to form a community around your offering. With the wisdom of the crowd on your side, you can't go wrong.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

Well, it depends; let’s first understand why you feel light bulbs are necessary.

(I’m kidding)

Actually, I had a light bulb moment yesterday—literally. We have a small chandelier in our entry way that blew its last bulb this past weekend, so my first order of business was to shed light on the situation (pun intended). Once I got up on the ladder, I realized I had a situation. I could not reach the light bulbs because there was a grey, metal diffuser in the way. It’s there so that people upstairs looking down don’t get blinded by staring directly into the bulbs. The only solution that came to mind was to remove the large, heavy, glass base of the contraption. So that’s what I did.

Before long, I was screaming to my wife for help. I’m balancing on the third step of a ladder holding a heavy, delicate ornament in one hand and the knobs that hold it in place in the other. Fortunately, Kim quickly came to the rescue and I was able to change out the light bulbs without breaking my neck.

Later that day, I stopped into the lighting store where we bought the chandelier and told my story to the owner. He patiently waited for me to finish my story, smiled, paused, then explained to me that I should have removed the diffuser—not the huge glass bowl at the bottom.

Good information not only increases strategic effectiveness and efficiency, but it also reduces risk. I talked about this yesterday when I was commenting on the awful bombings at the Boston Marathon. In my chandelier episode yesterday, I got the result I was looking for—light where there was no light. However, I could have arrived at the same result with much less risk, had I known about removing the diffuser instead of the base.

I’m still struggling to process how anyone thinks it’s okay to set off a bomb in the middle of a crowd of innocent people over a difference of ideals. I was in the dentist’s chair this afternoon when my wife sent me a text succinctly detailing the awful Boston Marathon bombing. I couldn’t believe it—and still can’t. It’s unfortunate that a plot like this actually succeeds; however, I’m thankful for all the terrorist plots against our people that don’t. Although I talk a lot about using information for strategy and innovation, information prowess is also a powerful tool to mitigate critical risks.

It’s hard to notice non-events because they aren’t conspicuous; however, it’s remarkable to think about all the terrorist plots that were attempted and failed. Our intelligence agencies work with our enforcement agencies around the clock to monitor and intercept all the crazy schemes devised to harm and kill Americans. At times like this, President Obama reminds us, our friends, and our enemies how serious we are about justice around these matters. The combination of leadership and information prowess keeps critical risks from surfacing. The unfortunate event in Boston today is the exception that makes the rule.

All strategy is vulnerable to the effects of critical risks—not only those that involve Big Data or some other form of information exploitation. Your degree of analytic capability has a direct impact on how well you mitigate these risks. You can see this in action with Santam, South Africa’s largest short-term insurance provider. With big data and predictive analytics, Santam was able to save millions that were previously lost to insurance fraud.

Mitigating critical risks is an important part of any leader’s strategy. If the stakes are high enough, it may make sense to assemble a big data team for the sole purpose of making sure nothing happens. Regardless, take some time today to see where advanced analytics might neutralize your biggest risks.

My sincere condolences to those affected by the Boston Marathon bombings

The right information is probably available, but are you sensing it? If not, this information is doing you no good. How in touch are you with your common sensors?

I just walked into my chiropractor’s office five minutes late. This is unusual for me, I'm usually very punctual. Unfortunately, I fell victim to my own informal control plan. No, I don’t track statistics on how long it takes to drive to my chiropractor; however, after going for several years I have an internal sense for the central tendency and variance of the drive time (a little Six Sigma lingo for you this morning). For good measure, I always leave 30 minutes prior to my appointment, which I did today.

As soon as I hit the freeway, I was in gridlock traffic. I thought there may be an accident; however, I didn’t see anything. It took a total of 35 minutes to make it to my appointment today; fortunately, my chiropractor wasn’t too upset.

What’s important to note, is the information for my travel time was available, I just wasn’t tuned in. Whenever you get directions on Google Maps today, not only does it tell you distance, but it also tells you driving time based on the current traffic. If I had a sensor to this information tied into my workflow engine, I would have known to leave a little bit earlier today.

Fortunately for me, this particular bridge isn’t critical to my daily operation or my strategic objectives; however, do you know what information you need to collect, and how timely it needs to be?

These are what I call common sensors. Sensors are the devices used to collect information. What makes them common is the fact that they should be baked into your organization. Don’t let the word common take away from their criticality. In fact common sensors are the most critical sensors you have. They drive your strategy and they drive your operations.

Know and instantiate your common sensors. It’s one thing to be late to the chiropractor—it’s another thing to be late to the market.

Scientists have now discovered a method for storing massive amounts of data in DNA. No, IT hasn’t invented yet another acronym for us to learn, I’m talking about actual Deoxyribonucleic Acid, the same macromolecules responsible for carrying all the genetic coding for all living organisms. According to a recent Wall Street Journal article, researchers have encoded a comic book (text and illustrations) onto engineered DNA strands, and have successfully decoded the strands back to the original text (an important test for usability!). Although a pricey flash drive can store about 250 billion bytes of data on a device the size of your thumb, that’s no match for DNA which can store at least 1000 times more data in the same space.

Of course, nobody’s running out to by DNA devices just yet, but who knows what the future holds? If it’s in your company’s DNA (pun intended) you might know what the future holds, or at least make a good run at shaping it. People now are trying to compare solid-state technology (e.g. flash drives) against the emerging DNA technology. There will of course be advances in solid state technology to meet the voracious appetite of data consumers which doubles every couple of years. Now with renewed possibilities for DNA storage, there will hopefully be advances in making the research more conducive to practical use.

It would be a great mistake for innovators to put DNA technology in the same category at solid-state technology. This is trying to solve an old problem with a new method. It’s one approach, but it’s boring, uncreative, and has only limited value. This thinking won’t build an innovative, breakthrough that brings a decisive competitive advantage. Doritos was a breakthrough, and still one of my favorite snacks. Ranch-flavored Doritos are pretty good, but not nearly as remarkable. Southwest Jalapeño Guacamole Doritos are silly—by this time all the value has evaporated and they’re just giving the people in New Product Development something to do. And, don’t get me started on Taco Bell.

Instead, consider looking for properties of the new technology that open up new possibilities. This concept sits at the heart of information innovation. For instance, DNA not only has dense storage capacity, but also has the ability to replicate and mutate rapidly. This could be used to solve complex problems that traditional computers cannot solve today. This is a quick idea off the top of my head, imagine what a concentrated exploration might uncover.

It all starts with asking the right questions, and committing your organization to finding an answer. You must be able to ask bold questions to get bold results. A breakthrough technology deserves a bold company to innovate a breakthrough product. Is it in your company’s DNA to rise to the challenge?

There’s plenty of speculation on what Google intends to do with the company, and its famous travel guide. There’s an obvious connection between this and the recent acquisition of Zagat, the restaurant guide, and travel software company ITA. Perhaps they’re making a strong move in the travel industry? They’re perhaps mounting an attack against the likes of Expedia and Orbitz?

Perhaps.

Google is full of surprises, and they always deliver on their promise of innovating in every corner possible.

What I like from an information strategy standpoint is that they’re not trying to bake everything in-house. They’ve certainly built an internal culture that fosters innovation, and information innovation is no exception. However, they also understand there’s more than one way to flip a floppy. When you start considering the best ways to exploit information, acquisition is a very solid strategy, as long as you have the competence for merging/acquiring businesses. Frommer’s is their 116th acquisition, and their stable includes some giants like YouTube, DoubleClick, and Motorola Mobility. By this time, I think they have the formula down.

“Buy versus build” decisions apply to more than just software. If there’s a company out there with valuable, proprietary information that you can exploit, why not make an offer? The worst they can say is no.

Excellent Management Systems, Inc.

"The Science Of Success"

John Weathington helps leaders transform organizations.

For over 20 years, John has consulted to people and firms of all sizes including Fortune 500 icons such as Chevron, Hewlett Packard, Sun Microsystems, Wells Fargo, PayPal, Cisco, Pacific Gas and Electric, Hitachi, and Visa where he managed the financial services giant's enterprise data strategy--a program consisting of 150 projects over 45 initiatives and 5 major tracks. Visit John at Excellent Management Systems, Inc. for news, updated information, client results, testimonials, free articles, and more.